Objectives: ：Although dysfunctional breathing is a common symptom in general population and affects qual-ities of life, it is still underdiagnosed. There are some studies of prevalence of it in astma, but few studies in anxiety and depressive disorders. The purposes of this study were to explore the prevalence of it in anxiety and depressive disorders, and to investigate whether anxiety and depressed mood influence it. Methods: ：135 patients diagnosed with anxiety or depressive disorders, and 124 controls were recruited. Ni-jmegen questionnaire was used to assess dysfunctional breathing, and Hospital anxiety depression scale was used. Results: ：The prevalence of dysfunctional breathing in anxiety or depressive disorders was higher than that in control. In the linear regression model, anxiety accounted for 59.6% of dysfunctional breathing, but depressed mood did not. With covariate adjusted for anxiety, scores of dysfunctional breathing in anxiety or depressive disorders were higher than in controls. Conclusions ：Dysfunctional breathing in anxiety or depressive disorders is higher than that in control. Adjust-ing anxiety, its difference is still. Anxiety affects dysfunctional breathing, but depressed mood does not.

Objective: In this study we investigated whether current mood states of patients with bipolar disorder have an influence on the screening accuracy of the Mood Disorder Questionnaire (MDQ). Methods: A total of 452 patients with mood disorder (including 192 with major depressive disorder and 260 with bipolar disorder completed the Korean version of the MDQ. Patients with bipolar disorder were subdivided into three groups (bipolar depressed only, bipolar euthymic only, bipolar manic/hypomanic only) according to current mood states. The screening accuracy of the MDQ including sensitivity, specificity and area under the curve (AUC) of receiver operating characteristic (ROC) curves were evaluated according to current mood states. Results: The optimal cutoff of MDQ was 5 in this study sample. Sensitivity and specificity were not significantly different according to current mood states. Significant differences in AUCs of four independent ROC curves were not found (ROC 1st curve included all bipolar patients; ROC 2nd curve included only bipolar depressed patients; ROC 3rd curve included only bipolar manic/hypomanic patients; ROC 4th curve included only bipolar euthymic patients). Conclusion The study results showed that current mood states (either euthymic state, depressed or manic/hypomanic) did not significantly influence the screening accuracy of the MDQ suggesting that the MDQ could be a useful screening instrument for detecting bipolar disorder in clinical practice regardless of the current mood symptoms of subjects.

Objective: To develop an evidence-based guideline for the diagnosis and treatment of antipsychotic-induced hyperprolactinemia by adapting existing high-quality clinical guidelines with a view to improve the clinical symptoms and long-term quality of life of patients by providing appropriate management. Methods: This guideline was developed according to the ADAPTE methodology. The adaptation process included determining key health questions, systematically searching and screening guidelines, evaluating the quality and contents of these guidelines, deriving recommendations for key questions, and performing a peer review. The selection criteria for the guideline search were (1) evidence-based guidelines, (2) published within the last 5 years, and (3) written in English or Korean. Results: After evaluating the quality and content, we finally selected three guidelines for adaptation. The final output of the development process was 25 recommendations for 10 key questions. We adopted the Agency for Health Research Quality methodology and presented the level of evidence from levels I to IV. In addition, we defined the recommendation grades from grade A (strongly recommended) to D (no recommendation) based on the level of evidence and clinical significance of the recommendation. Conclusion The development and dissemination of the adapted guideline is expected to increase the certainty of medical decision making and improve the quality of medical care. Further studies on the effectiveness and applicability of the developed guideline are necessary.

OBJECTIVES: To develop a tool which can easily access the hospital information system (HIS) to facilitate outpatient care and maximize patient satisfaction on his or her hospital visit. METHODS: Our Center for Informatics developed an outpatient guidance system (OGS) after careful analysis of the list of daily tasks undergone by patients and related work processes. Bluetooth beacons were installed to assist patients, to inform them of points of interest, and to guide them along the proper routes to and within the hospital. RESULTS: The OGS conveniently provided patients' clinic schedules, routes to the hospital, and direct costs; all of this information was embedded in the HIS accessed from patients' personal mobile devices or kiosks. Patients were also able to identify their locations within the hospital, receiving proper directions to subsequent task. Since its launch in October 2014, the number of mobile accesses increased from 4,011 to 8,242 per month within a year. CONCLUSIONS: The substantial growth of interest in and use of our OGS in such a short period indicate that this system has been successfully incorporated into patients' daily activities. We believe that this system will continue to help improve health services and the well-being of those visiting the hospital.
Ambulatory Care ; Appointments and Schedules ; Health Services ; Hospital Information Systems ; Humans ; Informatics ; Outpatients* ; Patient Care ; Patient Satisfaction ; Smartphone

Objective: This study investigated the prevalence and comorbidities of attention deficit hyperactivity disorder (ADHD) among adults and children/adolescents in Korea. Methods: This study used data from the Korea Health Insurance Review and Assessment Service collected from 2008 to 2018. Study participants comprised patients with at least one diagnosis of ADHD (International Statistical Classification of Diseases and Related Health Provisions, 10th revision code F90.0). Prevalence rates and psychiatric comorbidities were also analyzed. Results: We identified 878,996 patients diagnosed with ADHD between 2008 and 2018. The overall prevalence rate of diagnosed ADHD increased steeply from 127.1/100,000 in 2008 to 192.9/100,000 in 2018; it increased 1.47 times in children/adolescents (≤ 18 years) and 10.1 times in adults (＞ 18 years) during this period. Among adult and children/adolescent ADHD patients, 61.84% (95% confidence interval [95% CI] 61.74−61.93) and 78.72% (95% CI 78.53− 78.91) had at least one psychiatric comorbidity, respectively. Conclusion Our results showed that the prevalence rate of diagnosed ADHD has increased in Korea; however, it is lower than the global average. Further studies are required to identify and treat vulnerable populations appropriately.

Objective: Coronavirus disease 2019 (COVID-19) has psychological effects such as anxiety and depression as well as direct infection in people. The Fear of COVID-19 scale is a scale that can measure anxiety related to COVID-19 in a short time. The purpose of this study was to verify the reliability and validity the Korean version of Fear of COVID-19 scale (KF-COVID-19S). Methods: The data of total 186 normal adults and 17 patients were finally used for the statistical analysis. For internal consistency, Cronbach’s α was calculated. For concurrent and discriminant validity, the correlations with the Hospital Anxiety and Depression scale (HADS), Patient Health Questionnaire-15 (PHQ-15), World Health Organization Quality of Life Assessment Instrument Brief Form (WHOQOLBREF) were analyzed. For construct validity, exploratory and confirmatory factor analysis were conducted. Results: Cronbach alpha was 0.88. The two-factor model (factor 1: Physical fear, factor 2: Emotional fear) showed significantly positive correlations and appeared to be “good” fitness (CFI=0.906, IFI=0.907, NFI=0.902). Conclusion The KF-COVID-19S can be a useful scale that can measure the physical and emotional fears associated with COVID-19 in a short time. Because the psychiatric patients are a more vulnerable group to the fear, it is thought that the KF-COVID-19S will help to determine the patient’s level of anxiety and make a therapeutic plan for the underlying mental disorder.

Objective: Coronavirus disease 2019 (COVID-19) has psychological effects such as anxiety and depression as well as direct infection in people. The Fear of COVID-19 scale is a scale that can measure anxiety related to COVID-19 in a short time. The purpose of this study was to verify the reliability and validity the Korean version of Fear of COVID-19 scale (KF-COVID-19S). Methods: The data of total 186 normal adults and 17 patients were finally used for the statistical analysis. For internal consistency, Cronbach’s α was calculated. For concurrent and discriminant validity, the correlations with the Hospital Anxiety and Depression scale (HADS), Patient Health Questionnaire-15 (PHQ-15), World Health Organization Quality of Life Assessment Instrument Brief Form (WHOQOLBREF) were analyzed. For construct validity, exploratory and confirmatory factor analysis were conducted. Results: Cronbach alpha was 0.88. The two-factor model (factor 1: Physical fear, factor 2: Emotional fear) showed significantly positive correlations and appeared to be “good” fitness (CFI=0.906, IFI=0.907, NFI=0.902). Conclusion The KF-COVID-19S can be a useful scale that can measure the physical and emotional fears associated with COVID-19 in a short time. Because the psychiatric patients are a more vulnerable group to the fear, it is thought that the KF-COVID-19S will help to determine the patient’s level of anxiety and make a therapeutic plan for the underlying mental disorder.

OBJECTIVE: The aim of this study was to examine the validity of the Korean version of the Hypomania Checklist-32, second revision (HCL-32-R2) in mood disorder patients. METHODS: A total of 454 patients who diagnosed as mood disorder according to Structured Clinical Interview for DSM-IV Axis I Disorders, clinician version (SCID-CV) (bipolar disorder [BD] I, n=190; BD-II, n=72; and major depressive disorder [MDD], n=192) completed the Korean module of the HCL-32-R2 (KHCL-32-R2). RESULTS: The KHCL-32-R2 showed a three-factorial structure (eigenvalue >2) that accounted for 43.26% of the total variance. Factor 1 was labeled “active/elated” and included 16 items; factor 2, “irritable/distractible” and included 9 items; and factor 3 was labeled “risk-taking/indulging” and included 9 items. A score of 16 or more on the KHCL-32-R2 total scale score distinguished between BD and MDD, which yielded a sensitivity of 70% and a specificity of 70%. MDD and BD-II also could be differentiated at a cut-off of 15 with maximized sensitivity (0.67) and specificity (0.66). Cronbach’s alpha of KHCL-32-R2 and its subsets (factors 1, 2, and 3) were 0.91, 0.89, 0.81 and 0.79, respectively. Correlations between KHCL-32-R2 and Montgomery-Asberg Depression Rating Scale, Young Mania Rating Scale and Korean version of Mood Disorder Questionnaire were −0.66 (p=0.41), −0.14 (p=0.9), and 0.61 (p < 0.001), respectively. CONCLUSION: The KHCL-32-R2 may be a useful tool in distinguishing between bipolar and depressive patients in clinical settings.
Bipolar Disorder ; Depression ; Depressive Disorder, Major ; Diagnostic and Statistical Manual of Mental Disorders ; Humans ; Mood Disorders* ; Psychometrics* ; Sensitivity and Specificity